JP7633027B2 - Coin identification device and coin identification method - Google Patents

Description

This disclosure relates to a coin identification device and a coin identification method.

Conventionally, coin processing machines that perform processes such as coin counting are equipped with a coin identification device that has multiple sensors for identifying coins. In addition, bimetallic coins made of two or more types of materials, such as bicolor coins, are known in which the material of the center of the coin is different from that of the outer edge surrounding the center.

For example, Patent Document 1 discloses a coin identification device that includes a ring detection sensor, which is a magnetic sensor with a resonant circuit connected to a coil that detects the material of the outer edge of the coin, and a core detection sensor, which is a magnetic sensor with a resonant circuit connected to a coil that detects the material of the center of the coin, and that determines whether or not the coin is a bimetallic coin based on the outputs of the ring detection sensor and the core detection sensor.

Patent document 2 also discloses a coin identification device that includes a reflective magnetic sensor with a primary coil and a secondary coil, and detects bicolor coins from the output of the secondary coil when a high-frequency (250 kHz) signal is applied to the primary coil.

JP 2017-220119 A Patent No. 4157335

There are monometallic coins on the market that are made of the same material as the core of bimetallic coins and are very similar in size to the bimetallic coins, and there is a need, particularly overseas, to accurately detect the material of the core and outer edge of bimetallic coins.

However, in the coin identification device disclosed in Patent Document 1, as shown in FIG. 2 of Patent Document 1, in order to detect the material of the outer edge and center of the coin, it is necessary to place a ring detection sensor 32 and a core detection sensor 33, which have different frequencies, at positions where the outer edge and center of the bimetal coin are transported, and therefore space and control circuits are required to place these sensors, leaving room for improvement in terms of space and cost.

In the coin identification device disclosed in Patent Document 2, a high-frequency signal of 250 kHz is applied to the primary coil of the reflective magnetic sensor, but it is difficult to increase the frequency of the signal applied to the primary coil with a reflective magnetic sensor, and it is not easy to actually measure bicolor coins with a reflective magnetic sensor at this frequency level, making it difficult to actually commercialize it. This is because the higher the frequency of the signal applied to the primary coil, the more difficult it becomes to detect the phase shift between the signal applied to the primary coil and the signal detected by the secondary coil.

This disclosure has been made in consideration of the above-mentioned current situation, and aims to provide a coin identification device and coin identification method that can accurately identify bimetal coins with a simpler configuration.

In order to solve the above-mentioned problems and achieve the objective, the coin identification device of the present disclosure includes a magnetic sensor including a resonant coil that detects the magnetic characteristics of the coin being transported, a first waveform generation unit that generates first waveform data representing a material feature, which is a feature that changes depending on the material of the coin, from the output of the magnetic sensor, a first material detection unit that detects a first material feature corresponding to the outer edge of the coin from the first waveform data, a second material detection unit that detects a second material feature corresponding to the center of the coin from the first waveform data, and an identification processing unit that performs a coin identification process based on the first material feature and the second material feature.

The first material detection unit may include a first-order differential processing unit that performs differential processing on the first waveform data to generate first-order differential data, and a second-order differential processing unit that performs differential processing on the first-order differential data to generate second-order differential data, and may detect the first material feature based on the first-order differential data and the second-order differential data.

The first material detection unit may detect a first point where a differential value in the first-order differential data is less than a predetermined first threshold value and where a differential value in the second-order differential data exceeds a predetermined second threshold value.

The first material detection unit may detect the first material feature based on a plurality of material feature values from the first point to the second point in the first waveform data when a predetermined number of consecutive points from the first point to the second point have a differential value less than the first threshold value in the first-order differential data.

The first material detection unit may detect, as the first material feature, a material feature of the first waveform data at a point in the first derivative data where the derivative value is smallest among the predetermined number of points from the first point to the second point.

The coin identification device may further include a second waveform generating unit that generates second waveform data representing a distance feature, which is a feature that changes depending on the unevenness of the coin, from the output of the magnetic sensor.

The second material detection unit may detect a third point and a fourth point in the second waveform data corresponding to both ends of the coin in the conveying direction, and may detect the material characteristic value of a center point between a fifth point and a sixth point corresponding to the third point and the fourth point, respectively, in the first waveform data as the second material characteristic value.

The identification processing unit may determine whether the coin is a bimetal coin based on the first material characteristic amount and the second material characteristic amount.

The coin identification device may further include a memory unit that stores reference data relating to the material characteristics of the outer edge and center of a genuine bimetal coin, and the identification processing unit may compare the first material characteristic and the second material characteristic with the reference data.

The identification processing unit may determine that the coin is the genuine bimetal coin if the first material characteristic and the second material characteristic match the reference data.

The identification processing unit may determine the denomination of the coin by comparing the first material characteristic and the second material characteristic with the reference data.

The bimetal coin may be at least one of a bicolor coin and a bicolor clad coin.

The bimetallic coin may be a bicolor coin.

The coin identification method according to the present disclosure is a coin identification method using a magnetic sensor, the magnetic sensor including a resonant coil, and includes the steps of: detecting the magnetic characteristics of the transported coin by the magnetic sensor; generating first waveform data representing a material characteristic, which is a characteristic that changes depending on the material of the coin, from the output of the magnetic sensor; detecting a first material characteristic corresponding to the outer edge of the coin from the first waveform data; detecting a second material characteristic corresponding to the center of the coin from the first waveform data; and performing a coin identification process based on the first material characteristic and the second material characteristic.

The step of detecting the first material characteristic may include a step of differentiating the first waveform data to generate first-order differential data, and a step of differentiating the first-order differential data to generate second-order differential data, and the first material characteristic may be detected based on the first-order differential data and the second-order differential data.

The step of detecting the first material feature may include detecting a first point where a differential value in the first-order differential data is less than a predetermined first threshold value and where a differential value in the second-order differential data exceeds a predetermined second threshold value.

The step of detecting the first material feature may include detecting the first material feature based on a plurality of material features from the first point to the second point in the first waveform data when a predetermined number of consecutive points from the first point to the second point have a differential value less than the first threshold value in the first-order differential data.

The step of detecting the first material feature may include detecting, as the first material feature, the material feature of the first waveform data at a point in the first derivative data where the derivative value is the smallest among the predetermined number of points from the first point to the second point.

The coin identification method may further include a step of generating second waveform data representing a distance feature, which is a feature that changes depending on the unevenness of the coin, from the output of the magnetic sensor.

The step of detecting the second material characteristic may include detecting a third point and a fourth point in the second waveform data corresponding to both ends of the coin in the conveying direction, and may also detect the material characteristic of a center point between a fifth point and a sixth point corresponding to the third point and the fourth point, respectively, in the first waveform data as the second material characteristic.

The step of performing the identification process may determine whether the coin is a bimetal coin based on the first material characteristic and the second material characteristic.

The step of performing the identification process may involve comparing the first material characteristic and the second material characteristic with reference data relating to the material characteristic of the outer edge and center of a genuine bimetal coin, respectively.

The step of performing the identification process may determine that the coin is a genuine bimetal coin if the first material characteristic and the second material characteristic match the reference data.

The step of performing the identification process may determine the denomination of the coin by comparing the first material characteristic and the second material characteristic with the reference data.

The bimetal coin may be at least one of a bicolor coin and a bicolor clad coin.

The bimetallic coin may be a bicolor coin.

This disclosure provides a coin identification device and a coin identification method that can accurately identify bimetal coins with a simpler configuration.

(a) is a schematic plan view showing the coin face of a bi-color coin, and (b) is a schematic cross-sectional view of the bi-color coin. (a) is a schematic plan view showing the coin surface of a clad coin (plated), (b) is a schematic cross-sectional view of a clad coin (plated), (c) is a schematic plan view showing the coin surface of a clad coin (three-layer structure), and (d) is a schematic cross-sectional view of a clad coin (three-layer structure). FIG. 2A is a schematic plan view showing the coin face of a bi-color clad coin, and FIG. 2B is a schematic cross-sectional view of the bi-color clad coin. FIG. 1 is a schematic diagram illustrating the configuration of a coin identification device according to a first embodiment, showing a coin transport path as viewed from above. FIG. 5 is a schematic diagram for explaining the configuration of the coin discriminating device according to the first embodiment, and is a cross-sectional view taken along line AB in FIG. 1 is a block diagram illustrating an example of a configuration of a coin identification device according to a first embodiment. FIG. 4 is a graph illustrating an example of first waveform data generated by a first waveform generating section. 1 is a graph showing the relationship between the material (conductivity) of a coin and the material feature amount. FIG. 2 is a block diagram illustrating a configuration of a first material detection unit according to the first embodiment. 10 is a graph illustrating another example of the first waveform data generated by the first waveform generating section. 4 is a graph showing an example of first waveform data, its first-order differential data, and its second-order differential data. 5 is a flowchart illustrating an example of an operation of the coin identification device according to the first embodiment. 6 is a flowchart illustrating an example of an operation of a first material detection unit according to the first embodiment. 1 is a block diagram illustrating an example of a configuration of a magnetic sensor according to a first embodiment. FIG. 4 is a block diagram illustrating another example of the configuration of the coin identification device according to the first embodiment. 10 is a flowchart illustrating another example of the operation of the coin identification device according to the first embodiment. FIG. 11 is a block diagram illustrating an example of the configuration of a coin identification device according to a second embodiment. 6 is a graph illustrating an example of second waveform data generated by a second waveform generating section. 13 is a graph showing the relationship between the distance between the coin and the resonance coil and the distance feature amount. 10 is a graph illustrating another example of the first waveform data generated by the first waveform generating section. 10 is a flowchart illustrating an example of an operation of the coin identification device according to the second embodiment. 10 is a flowchart illustrating an example of an operation of a second material detection unit according to the second embodiment. FIG. 11 is a block diagram illustrating another example of the configuration of the coin identification device according to the second embodiment. 10 is a flowchart illustrating another example of the operation of the coin identification device according to the second embodiment. 4 is a graph showing an example of first waveform data, its first-order differential data, and its second-order differential data.

Embodiments of a coin identification device and a coin identification method according to the present disclosure will be described below with reference to the drawings. In the following, the present disclosure will be described using as an example a coin identification device and a coin identification method that are targeted at coins as currency, but the coins that are the subject of the present disclosure include not only currency coins but also coins used in gaming machines. Note that the following description is an example of a coin identification device and a coin identification method.

The coin identification device and coin identification method disclosed herein are suitable for bimetallic coins, which are coins made of two or more types of material, but can also identify monometallic coins, which are coins made of one type of material. The following mainly describes the case of identifying bimetallic coins.

First, bimetallic coins will be described. FIG. 1(a) is a schematic plan view showing the coin surface of a bicolor coin, and FIG. 1(b) is a schematic cross-sectional view of the bicolor coin. FIG. 2(a) is a schematic plan view showing the coin surface of a clad coin (plated), FIG. 2(b) is a schematic cross-sectional view of a clad coin (plated), FIG. 2(c) is a schematic plan view showing the coin surface of a clad coin (three-layer structure), and FIG. 2(d) is a schematic cross-sectional view of a clad coin (three-layer structure). FIG. 3(a) is a schematic plan view showing the coin surface of a bicolor clad coin, and FIG. 3(b) is a schematic cross-sectional view of a bicolor clad coin. Bimetallic coins include bicolor coins, clad coins, and bicolor clad coins, but bicolor coins and bicolor clad coins are preferred, and bicolor coins are particularly preferred. As shown in FIG. 1(a) and FIG. 1(b), the bicolor coin 51 is formed by inserting the core part 51a into the ring part 51b, which is made of different materials (metal or alloy) for the circular core part 51a in the center and the ring part 51b in the outer edge part surrounding the core part 51a. As shown in FIG. 2(a) and FIG. 2(b), the clad coin 52 is formed by using different materials (metal or alloy) for the core material 52a and the surface layer 52b covering the core material 52a. For example, the clad coin 52 is plated and stamped on the circular core material (base material) 52a. As shown in FIG. 2(c) and FIG. 2(d), the clad coin 52 may be formed by punching a three-layer plate into a circle to form the surface layer 52b, the core material 52a, and the surface layer 52b, and stamping. As shown in FIG. 3(a) and FIG. 3(b), the bicolor clad coin 53 is formed by fitting the core part 53a into the ring part 53b, which is made of different materials (metals or alloys) for the circular core part 53a in the center having the structure of a clad coin and the ring part 53b on the outer edge surrounding the core part 53a. The core part 53a in the bicolor clad coin 53 is formed by using different materials (metals or alloys) for the core material 53a1 and the surface layer 53a2 covering the core material 53a1. The surface layer 53a2 of the core part 53a may be plated, or may be punched into a circular shape from a three-layer plate.

Hereinafter, the central portion (core portion) and outer edge portion (ring portion) of the bicolor or bicolor clad coin may be referred to as the inner portion and outer portion of the bimetal coin, respectively.

In the following description, the same reference numerals will be used appropriately across different embodiments and drawings for components that have the same or similar functions, and repeated descriptions of those components will be omitted as appropriate.

First Embodiment
In the first embodiment, a high-frequency magnetic sensor is placed at a position where the inner and outer parts of the bimetal coin pass, and the positions of the inner and outer parts are estimated from the sensor output from the time the coin arrives to the time it passes, and their material features are obtained, thereby enabling material detection of the bimetal coin using a single sensor.

Figures 4 and 5 are schematic diagrams explaining the configuration of the coin identification device according to the first embodiment, where Figure 4 is a diagram of the coin transport path seen from above, and Figure 5 is a cross-sectional view taken along line A-B in Figure 4. Figures 4 and 5 show XYZ coordinate systems which are orthogonal to each other. As shown in Figures 4 and 5, the coin identification device 1 according to this embodiment has a single magnetic sensor 10 arranged on the transport path 110 of the coin processing device.

The coins C are transported one by one at intervals in the transport direction (+X direction in FIG. 4) on the transport path 110 of the coin processing device by a transport means (not shown, for example fins) of the coin processing device. The transport path 110 has a smooth transport surface 111 that supports the underside of the coin C, and a guide surface 112 that contacts the peripheral surface of the coin C and guides the coin C to one side. The transport surface 111 is parallel to the XY plane in FIG. 4, and the coin C is transported on the transport surface 111 in a state where it is biased to the end of the transport path 110 on the guide surface 112 side (in the -Y direction in FIG. 4), i.e., in a state where it is in contact with the guide surface 112.

The magnetic sensor 10 includes a resonant coil 11 (hereinafter, sometimes simply referred to as coil 11) arranged below the transport path 110 (-Z direction in FIG. 5), and detects the magnetic characteristics of the coin C transported along the transport path 110. The magnetic sensor 10 including the resonant coil 11 can apply a higher frequency signal to the coil 11 compared to a reflective magnetic sensor, and even in this case, the material (material characteristic amount) of each part of the coin can be detected from the output of the magnetic sensor 10, as described below. In addition, a more complex circuit is not required as a control circuit for the magnetic sensor 10 compared to the conventional one, and for example, a circuit for a ring detection sensor or core detection sensor disclosed in Patent Document 1 can be used. Therefore, in this embodiment, bimetal coins can be detected with a simpler sensor configuration and circuit configuration than the conventional one, which allows space saving and cost reduction.

The magnetic sensor 10 also includes a cylindrical pot core 12, as shown in FIG. 5. The pot core 12 is an E-shaped core made of a magnetic material in cross section, and a winding is wound around the central axis of the pot core 12 to form the coil 11. In the conveying direction of the coin C, the width of the coil 11 (width in the X direction) is designed to be smaller than the widths of the inner part Ba and outer part Bb of the bimetal coin B.

The magnetic sensor 10 generates a magnetic field (magnetic flux) in the transport path 110 according to the oscillation frequency provided by the resonant circuit (see FIG. 13 described later), and detects changes in the magnetic field when a coin C passes through the transport path 110. The resonant circuit is connected to the coil 11, and resonates with the coil 11 at a high frequency suitable for detecting the material of the coin C. Specifically, it resonates at a frequency of 1 to 2 MHz, more specifically 1.4 to 1.5 MHz. This allows the material near the surface of the coin C to be effectively detected, making it possible to more effectively detect bimetallic coins, and in particular bicolor clad coins. In addition, the ability to detect materials in a narrow area allows the resolution of the magnetic sensor 10 to be increased.

The coil 11 of the magnetic sensor 10 is arranged with a gap between it and the guide surface 112 in the width direction of the conveying path 110. Therefore, when the coin C is conveyed with one side biased toward the guide surface 112, one outer edge, the center, and the other outer edge of the coin C pass over the coil 11 (within the magnetic field generated by the coil 11) in sequence, and the output signal of the magnetic sensor 10 also changes. That is, the magnetic sensor 10 outputs a signal according to the material of the outer edge and center of the coin C. In this way, the magnetic sensor 10 detects the material of the inner part Ba and the outer part Bb of the bimetal coin B. If the coin C is made of a non-magnetic material, when the coin C arrives on the coil 11, the output of the magnetic sensor 10 is attenuated (the amplitude is reduced), and the higher the conductivity of the coin C, the greater the attenuation rate of the output.

Figure 6 is a block diagram illustrating an example of the configuration of the coin identification device according to the first embodiment. As shown in Figure 6, the coin identification device 1 according to this embodiment includes, in addition to the magnetic sensor 10, a first waveform generating unit 21, a first material detection unit 22, a second material detection unit 23, and an identification processing unit 24. The first waveform generating unit 21, the first material detection unit 22, the second material detection unit 23, and the identification processing unit 24 function by executing their respective corresponding programs by the control unit 20 described below.

The first waveform generating unit 21 generates first waveform data representing the material feature from the output of the magnetic sensor 10. More specifically, the first waveform generating unit 21 generates the first waveform data based on the output of the magnetic sensor 10 at different successive timings. Therefore, the first waveform data indicates the temporal change in the material feature. For example, the first waveform data may be generated based on a time series (digital signal) obtained by sampling the output (analog signal) of the magnetic sensor 10 at a predetermined time interval. The material feature is a feature that changes depending on the material of the coin, in particular the surface material.

Figure 7 is a graph that shows a schematic example of the first waveform data generated by the first waveform generating unit. Figure 7 shows an example of when a bimetal coin is detected, and the upper part shows a side cross section of bimetal coin B (inner part Ba and outer part Bb) at the corresponding position on the graph. As shown in Figure 7, the first waveform data is data that represents material characteristics with respect to time, and the horizontal axis of Figure 7 represents the time direction. However, since the magnetic sensor 10 normally detects coins that are transported at a predetermined speed, the first waveform data is also data that represents material characteristics with respect to the position in the coin transport direction, and in this case, the horizontal axis of Figure 7 represents the position in the coin transport direction.

Also, as shown in FIG. 7, the material characteristic tends to show extreme values or become stable points where the value hardly fluctuates at the entry point of the coin (bimetal coin B), the center of the outer part Bb of the bimetal coin B, the joint where the inner part Ba and outer part Bb of the bimetal coin B are joined, and the center of the coin's center (inner part Ba). Using this characteristic, the positions of the inner part Ba and outer part Bb of the bimetal coin B are detected from the material characteristic.

Figure 8 is a graph showing the relationship between the material (conductivity) of a coin and the material characteristic amount. The first waveform data is generated, for example, by the following method. When a coin is transported near the resonant coil 11, the magnetic sensor 10 outputs a change in the voltage of the resonant circuit and a change in the oscillation frequency of the resonant circuit. These change depending on the material of the coin, particularly the conductivity. Furthermore, the material characteristic amount changes depending on the conductivity of the coin. From these, the material characteristic amount can be obtained from the output of the magnetic sensor 10. In other words, there is a predetermined relationship between the voltage of the resonant circuit, the oscillation frequency of the resonant circuit, and the material (conductivity) of the coin. Furthermore, there is a predetermined relationship between the material (conductivity) of the coin and the material characteristic amount. Specifically, as shown in Figure 8, the higher the conductivity of the coin, the smaller the material characteristic amount. Therefore, a table based on the three-way relationship between the voltage and oscillation frequency of the resonant circuit and the material (conductivity) of the coin, and a table showing the relationship between the material (conductivity) of the coin and the material characteristic amount are prepared in advance, and the material (conductivity) of the coin is obtained by comparing the output of the magnetic sensor 10, i.e., the voltage and oscillation frequency of the resonant circuit, with the former table, and the material characteristic amount of the coin is obtained by comparing the material (conductivity) with the latter table. The first waveform data can be generated by using the voltage and oscillation frequency of the resonant circuit related to the output of the magnetic sensor 10 at successive different timings and these tables to obtain the material characteristic amount at each timing. The relationship shown in FIG. 8 was obtained in advance by collecting data using various coins with different materials (conductivities).

The first material detection unit 22 detects a first material feature value corresponding to the outer edge of the coin, in this case the outer part of the bimetal coin, from the first waveform data. The second material detection unit 23 detects a second material feature value corresponding to the center of the coin, in this case the inner part of the bimetal coin, from the first waveform data. As described above, the first waveform data has the property of showing characteristic changes depending on each part of the bimetal coin, so the first material detection unit 22 can accurately detect the material feature value of the point corresponding to the outer part of the bimetal coin as the first material feature value, and the second material detection unit 23 can accurately detect the material feature value of the point corresponding to the inner part of the bimetal coin as the second material feature value.

The identification processing unit 24 performs a coin identification process based on the first material characteristic amount and the second material characteristic amount detected by the first material detection unit 22 and the second material detection unit 23, respectively. Since the first material characteristic amount and the second material characteristic amount accurately indicate the material characteristic amount at the points corresponding to the outer part and the inner part of the bimetal coin, respectively, the identification processing unit 24 can accurately identify the bimetal coin.

For example, the identification processing unit 24 may determine whether a coin is a bimetal coin based on the first material characteristic amount and the second material characteristic amount. It may also determine the material of the outer part and the inner part of the bimetal coin.

As a result, this embodiment makes it possible to accurately identify bimetal coins with a simpler configuration than conventional methods.

Figure 9 is a block diagram illustrating the configuration of the first material detection unit according to the first embodiment. As shown in Figure 9, the first material detection unit 22 may include a first-order differential processing unit 22a that performs differential processing on the first waveform data to generate first-order differential data, and a second-order differential processing unit 22b that performs differential processing on the first-order differential data to generate second-order differential data, and may detect a first material feature based on the first-order differential data and the second-order differential data.

Figure 10 is a graph that shows a schematic representation of another example of the first waveform data generated by the first waveform generating unit. Figure 10 shows an example when a bimetal coin is detected. Figure 11 is a graph that shows a schematic representation of an example of the first waveform data, its first derivative data, and its second derivative data. Figure 11 shows an example when a bimetal coin is detected, and in the upper part, a side cross section of bimetal coin B (inner part Ba and outer part Bb) is illustrated at the corresponding position on the graph. The first waveform data in Figure 11 corresponds to the first waveform data sandwiched between the dashed lines in Figure 10. In the example shown in Figure 10, the first waveform data shows a stable region with small value fluctuations in the region corresponding to the center of the outer part and the center of the inner part of the bimetal coin (the region surrounded by a circle in the figure).

As shown in FIG. 11, the first-order differential data represents the gradient information of the first waveform data (the gradient of the waveform), and the second-order differential data represents the gradient strength of the first waveform data (the rate of change in the gradient of the waveform) and the gradient direction (whether the waveform rotates clockwise or counterclockwise). When the sign of the second-order differential data changes, the rotation direction of the waveform of the first waveform data changes. Therefore, the gradient of the waveform of the first waveform data can be quantified and its shape can be evaluated. In addition, in a bimetal coin B whose inner part Ba and outer part Bb are made of different materials, a stable region in which the gradient of the waveform is small and the first waveform data changes stably exists in the region corresponding to the outer part Bb. Therefore, by including the first-order differential processing unit 22a and the second-order differential processing unit 22b, it is possible to obtain the first material characteristic value more accurately from the material characteristic value of the stable region, and the material characteristic of the outer part Bb can be detected with high accuracy.

The first-order differential processing unit 22a may simultaneously perform differential processing on the first waveform data and noise removal (e.g., sine wave filter correction processing) on the first waveform data. Similarly, the second-order differential processing unit 22b may simultaneously perform differential processing on the first-order differential data and noise removal (e.g., sine wave filter correction processing) on the first-order differential data.

As shown in FIG. 11, the first material detection unit 22 may detect a first point P1 where the differential value in the first-order differential data is less than a predetermined first threshold value Th1 and where the differential value in the second-order differential data exceeds a predetermined second threshold value Th2. This makes it possible to prevent an area including an extreme value of the first waveform data caused by the joint of the bimetal coin B (see FIG. 7) from being mistakenly detected as a stable area corresponding to the outer part Bb of the bimetal coin B. Therefore, the stable area corresponding to the outer part Bb of the bimetal coin B can be detected more reliably, and the material characteristics of the outer part Bb can be detected with higher accuracy.

The first threshold value Th1 can be set as appropriate, but can be, for example, 30-50% (e.g., 40%) of the minimum differential value of the first-order differential data (e.g., Min in FIG. 11). By setting it to 30-50%, it is possible to make it less susceptible to the effects of fluctuations in the coin conveying speed, so that the material characteristics of the outer part Bb can be detected with higher accuracy even if the coin conveying speed fluctuates. The second threshold value Th2 can be set as appropriate, but can be, for example, 5-25% (e.g., 10%) of the maximum differential value of the second-order differential data (e.g., Max in FIG. 11).

More specifically, the first material detection unit 22 may search for points that are less than the first threshold value Th1 in order from the beginning of the first-order differential data, and when a point that is less than the first threshold value Th1 is found, it may determine whether or not the differential value of the second-order differential data at that point exceeds the second threshold value Th2. If it does exceed the second threshold value Th2, the point may be determined as the first point P1. If it does not exceed the second threshold value Th2, the search process and determination process may be repeated in the same manner for the first-order differential data from that point onwards until the first point P1 is detected.

As shown in FIG. 11, the first material detection unit 22 may detect a first material characteristic based on a plurality of material characteristic values from the first point P1 to the second point P2 in the first waveform data when there are a predetermined number n (n is a natural number equal to or greater than 2) of consecutive points from the first point P1 to the second point P2 in the first derivative data where the derivative value is less than the first threshold value Th1. This effectively prevents momentary noise from being erroneously detected as a stable region and allows the stable region of the first waveform data to be detected more effectively, thereby enabling the material characteristic of the outer part Bb of the bimetal coin B to be detected with even higher accuracy. The predetermined number n from the first point P1 to the second point P2 can be set appropriately and may be, for example, 3 to 10 (e.g., 5).

More specifically, the first material detection unit 22 may perform the following process. That is, first, the point number count is set to 1 (first step). Next, it is determined whether the point number count is smaller than a predetermined number n (second step). If it is smaller (second step: Yes), it is determined whether the differential value of the first-order differential data of the point next to the first point P1 is smaller than the first threshold value Th1 (third step). If it is smaller than the first threshold value Th1 (third step: Yes), the point number count is incremented by +1 (fourth step) and the process returns to the second step. If it is determined in the second step that the point number count is not smaller than the predetermined number n (second step: No), the point last determined in the third step to be smaller than the first threshold value Th1 is set as the second point P2, and the first material characteristic amount is detected based on the multiple material characteristic amounts from the first point P1 to the second point P2. If it is determined in the third step that the first material is not less than the first threshold Th1 (third step: No), the first material detection unit 22 may newly detect the first point P1 as described above.

As shown in FIG. 11, the first material detection unit 22 may detect, as the first material characteristic, the material characteristic amount (SMx in FIG. 11) of the first waveform data at the point (Px in FIG. 11) where the differential value is the smallest among a predetermined number n of points from the first point P1 to the second point P2 in the first-order differential data. This makes it possible to detect a material characteristic amount where the fluctuation of the first waveform data is particularly small even in the stable region of the first waveform data, and therefore makes it possible to detect the material characteristic of the outer part Bb of the bimetal coin B with particularly high accuracy.

The second material detection unit 23 may detect the second material characteristic corresponding to the center of the coin, in this case the inner part of the bimetal coin, from the first waveform data, for example, by the following method. That is, first, the first waveform data is monitored, and the point at which the material characteristic has changed by more than a predetermined amount from the material characteristic at the time of standby before the coin arrives at the magnetic sensor 10 is determined to be the "medium in" point, and if there are a predetermined number of consecutive points after the medium in point where the material characteristic is the same as at the time of standby, the first of the predetermined number of consecutive points is determined to be the "medium out" point. Then, the material characteristic at the center point between the "medium in" point and the "medium out" point is detected as the second material characteristic.

Next, the operation of the coin identification device according to this embodiment will be described with reference to FIG. 12. FIG. 12 is a flowchart illustrating an example of the operation of the coin identification device according to the first embodiment.

As shown in FIG. 12, first, the magnetic sensor 10 detects the magnetic characteristics of the coin being transported (step S11).

Next, the first waveform generating unit 21 generates first waveform data representing the material characteristic from the output of the magnetic sensor 10 (step S12).

Next, the first material detection unit 22 detects a first material feature corresponding to the outer edge of the coin (the outer part of the bimetal coin) from the first waveform data generated in step S12 (step S13).

Next, the second material detection unit 23 detects a second material feature corresponding to the center of the coin (the inner part of the bimetal coin) from the first waveform data generated in step S12 (step S14).

Note that the timing of steps S13 and S14 may be reversed or may be simultaneous.

Then, the recognition processing unit 24 performs a coin recognition process based on the first material characteristic detected in step S13 and the second material characteristic detected in step S14 (step S15), and the operation of the coin recognition device 1 is completed.

FIG. 13 is a flowchart illustrating an example of the operation of the first material detection unit according to the first embodiment. The first material detection unit 22 (step S13) may detect the first material feature amount according to the flow shown in FIG. 13.

In this case, first, the first-order differential processing unit 22a performs differential processing on the first waveform data to generate first-order differential data (step S21).

Next, the second-order differential processing unit 22b performs differential processing on the first-order differential data to generate second-order differential data (step S22).

Next, the first material detection unit 22 detects a first point P1 where the differential value in the first-order differential data is less than a predetermined first threshold value and where the differential value in the second-order differential data exceeds a predetermined second threshold value (step S23).

Then, the first material detection unit 22 determines whether or not there are a predetermined number n of consecutive points from the first point P1 to the second point P2 where the differential value is less than the first threshold value (step S24). If there are a predetermined number of consecutive points (step S24: Yes), the first material feature is detected based on the multiple material feature values from the first point P1 to the second point P2 in the first waveform data (step S25), and the process of detecting the first material feature is completed.

In step S25, for example, the first material detection unit 22 detects the material feature SMx of the first waveform data at point Px, which is the smallest derivative value among the points from the first point P1 to the second point P2 in the first-order differential data, as the first material feature.

In step S24, if there are no consecutive points from the first point P1 where the differential value is less than the first threshold value for the predetermined number n (step S24: No), the first material detection unit 22 repeats the processes of steps S23 and S24 until there are consecutive points from the first point P1 to the second point P2 where the differential value is less than the first threshold value for the predetermined number n. That is, the first material detection unit 22 detects a new first point P1 that satisfies the above condition from the data after the points detected up to step S24 (step S23), and determines whether there are consecutive points from the new first point P1 to the second point P2 where the differential value is less than the first threshold value for the predetermined number n (step S24).

Next, we will explain a more specific embodiment of the coin identification device 1 according to this embodiment.

FIG. 14 is a block diagram illustrating an example of the configuration of the magnetic sensor according to the first embodiment. In addition to the resonant coil 11, the magnetic sensor 10 may further include a resonant circuit 13 and a detection circuit 14, as shown in FIG. 14.

The resonant circuit (LC resonant circuit) 13 is connected to the coil 11 and includes an AC power source connected to the coil 11 for exciting the coil 11, and a capacitor connected in parallel to the coil 11. The frequency of the AC power source is set to an oscillation frequency (resonant frequency) specific to the coil 11 and the resonant circuit 13, and the resonant circuit 13 resonates with the coil 11 at a high frequency (specifically, 1 to 2 MHz, more specifically, 1.4 to 1.5 MHz) suitable for detecting the material of the coin, as described above. In this way, the resonant circuit 13 applies an AC voltage (sine wave) to the coil 11, generating an AC magnetic flux in the transport path 110. When a coin enters this magnetic flux, an induced current (eddy current) is generated in the coin, changing the magnetic flux. As a result, the output (voltage and oscillation frequency) of the resonant circuit 13 changes in response to this change in magnetic flux.

The detection circuit 14 is connected to the resonant circuit 13 and includes an amplifier circuit that amplifies the output of the resonant circuit 13 and a DC conversion circuit that converts the output (AC) of the amplifier circuit into DC.

Figure 15 is a block diagram illustrating another example of the configuration of the coin identification device according to the first embodiment. In addition to the above-mentioned magnetic sensor 10, first waveform generating unit 21, first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), second material detection unit 23, and identification processing unit 24, the coin identification device 1 may further include an AD converter 30, a memory unit 40, and a control unit (arithmetic processing unit) 20, as shown in Figure 15.

The AD converter 30 is connected to the magnetic sensor 10 (detection circuit 14), samples the analog signal input from the detection circuit 14 of the magnetic sensor 10 at predetermined time intervals, and converts it into a time series (digital signal). Sampling by the AD converter 30 is performed for a period from before the coin arrives at the magnetic sensor 10 until after it has passed through the magnetic sensor 10.

The storage unit 40 is configured from storage devices such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, HDD (Hard Disk Drive), SSD (Solid State Drive), etc., and is configured to be able to write and read various data (e.g., thresholds, tables, reference data, etc.) and programs.

The control unit 20 controls each part of the coin identification device 1, and is composed of, for example, programs for implementing various processes, a CPU (Central Processing Unit) that executes the programs, and various hardware (for example, an FPGA (Field Programmable Gate Array)) that is controlled by the CPU.

The control unit 20 acquires the sampling data (time series) sampled by the AD converter 30 from the AD converter 30 and stores it in the memory unit 40.

The control unit 20 can function as the above-mentioned first waveform generating unit 21, first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), second material detection unit 23, and identification processing unit 24 by executing corresponding programs.

The first waveform generating unit 21 generates the first waveform data based on the sampling data stored in the memory unit 40, i.e., a time series obtained by sampling the output of the magnetic sensor 10 at a predetermined time interval.

The memory unit 40 may store reference data relating to the material characteristics of the outer and inner parts of a genuine bimetal coin, and the identification processing unit 24 may compare the first and second material characteristics detected by the first and second material detection units 22 and 23, respectively, with this reference data. This makes it possible to determine with high accuracy the denomination (and authenticity) of the coin, and whether the coin is a bimetal coin (particularly a bicolor coin or a bicolor clad coin).

In this case, the reference data may include an acceptable range of material characteristics that is set based on the material characteristics detected from the outer and inner parts of the genuine bimetal coin, and the identification processing unit 24 may compare the first material characteristic and the second material characteristic with the corresponding acceptable ranges, i.e., determine whether or not they are included in the corresponding acceptable ranges.

In addition, the identification processing unit 24 may determine that the coin is a genuine bimetal coin (e.g., a bicolor coin or a bicolor clad coin) if the first material characteristic and the second material characteristic match the reference data.

More specifically, the identification processing unit 24 may compare the first material characteristic amount and the second material characteristic amount with the corresponding tolerance ranges, and if they are within the corresponding tolerance ranges, determine that the coin is a genuine bimetal coin (e.g., a bicolor coin or a bicolor clad coin).

The identification processing unit 24 may also determine the denomination of the coin by comparing the first material characteristic and the second material characteristic with reference data.

In this case, the reference data may include an acceptable range of material features for each denomination that is set based on the material features detected from the outer edge and center of a genuine coin, and the identification processing unit 24 may compare the first material feature and the second material feature with the corresponding acceptable range for each denomination, i.e., determine whether or not they are included in the corresponding acceptable range. Then, if there is a denomination for which both the first material feature and the second material feature are included in the corresponding acceptable range, it may be determined that the coin is of that denomination.

The identification processing unit 24 may also compare the first material characteristic amount and the second material characteristic amount with each other to determine whether the material of the outer edge of the coin is the same as the material of the center of the coin. This allows the identification processing unit 24 to determine whether the coin is made of a single material, i.e., a monometallic coin (if they are the same), or whether it is made of two or more materials, i.e., a bimetallic coin (if they are not the same).

Next, the operation of the coin identification device according to this embodiment shown in FIG. 15 will be described with reference to FIG. 16. FIG. 16 is a flowchart illustrating another example of the operation of the coin identification device according to the first embodiment.

As shown in FIG. 16, first, similar to the case shown in FIG. 12, the magnetic sensor 10 detects the magnetic characteristics of the coin being transported (step S11).

Next, the AD converter 30 samples the output of the magnetic sensor 10 (the analog signal input from the detection circuit 14) and converts it into a time series (step S31).

Next, the first waveform generating unit 21 generates first waveform data representing material characteristics from the sampling data (time series) sampled by the AD converter 30 (step S32).

Then, steps S13 to S15 are executed as shown in FIG. 12, and the operation of the coin identification device 1 is completed.

The first material detection unit 22 (step S13) may detect the first material feature amount according to the flow shown in FIG. 13.

Furthermore, in step S15, as described above, the identification processing unit 24 may compare the first material characteristic amount and the second material characteristic amount with reference data, and may determine that the coin is a genuine bimetal coin if the first material characteristic amount and the second material characteristic amount match the reference data. The identification processing unit 24 may also determine the denomination of the coin by comparing the first material characteristic amount and the second material characteristic amount with the reference data. Furthermore, the identification processing unit 24 may compare the first material characteristic amount and the second material characteristic amount with each other to determine whether the material of the outer edge of the coin is the same as the material of the center of the coin.

Second Embodiment
This embodiment is substantially the same as the first embodiment, except that the present embodiment further includes a second waveform generating unit that generates second waveform data, and the second material detection unit detects the second material feature by utilizing the second waveform data. Fig. 17 is a block diagram illustrating an example of the configuration of a coin identification device according to the second embodiment.

As shown in FIG. 17, the coin identification device 2 of this embodiment includes a magnetic sensor 10, a first waveform generating unit 21, a first material detecting unit 22, a second material detecting unit 23, and an identification processing unit 24, similar to the first embodiment, and further includes a second waveform generating unit 25.

Note that the first material detection unit 22 may include a first-order differential processing unit 22a and a second-order differential processing unit 22b, as in the first embodiment.

The second waveform generating unit 25 generates second waveform data representing the distance feature from the output of the magnetic sensor 10. More specifically, the second waveform generating unit 25 generates the second waveform data based on the output of the magnetic sensor 10 at different successive timings. Therefore, the second waveform data indicates a temporal change in the distance feature. For example, the second waveform data may be generated based on a time series (digital signal) obtained by sampling the output (analog signal) of the magnetic sensor 10 at a predetermined time interval. The first waveform generating unit 21 and the second waveform generating unit 25 can generate the first waveform data and the second waveform data, respectively, based on the same output of the magnetic sensor 10, for example, based on the same time series. The distance feature is a feature that changes depending on the unevenness of the coin, particularly the unevenness of the surface on the magnetic sensor 10 side, and changes depending on the distance (spacing) between the coin and the resonant coil 11 of the magnetic sensor 10. The unevenness may be due to, for example, the marking or edging of the coin.

Figure 18 is a graph showing a schematic example of second waveform data generated by the second waveform generating unit. Figure 18 shows an example of when a bimetal coin is detected, and the upper part shows a side cross section of bimetal coin B (inner part Ba and outer part Bb) at the corresponding position on the graph. As shown in Figure 18, the second waveform data is data representing distance features with respect to time, and the horizontal axis of Figure 18 represents the time direction. However, since the magnetic sensor 10 normally detects coins transported at a predetermined speed, the second waveform data is also data representing distance features with respect to the position in the coin transport direction, and in this case, the horizontal axis of Figure 18 represents the position in the coin transport direction.

Also, as shown in FIG. 18, the distance feature value tends to change suddenly just after the medium is inserted and just before the medium is removed. That is, the distance feature value increases suddenly when the coin (bimetal coin B) begins to overlap the resonance coil 11, and decreases suddenly just before the coin (bimetal coin B) passes through the resonance coil 11. This characteristic is used to detect the positions of both ends of the coin from the distance feature value. Also, the distance feature value tends to decrease at the engagement portion of the bimetal coin B.

Figure 19 is a graph showing the relationship between the distance between the coin and the resonant coil and the distance feature amount. The second waveform data is generated, for example, by the following method. The magnetic sensor 10 outputs a change in the voltage of the resonant circuit and a change in the oscillation frequency of the resonant circuit as the coin is transported near the resonant coil 11. These change depending on the distance (spacing) between the coin and the resonant coil 11. The distance feature amount also changes depending on the distance between the coin and the resonant coil 11. From these, the distance feature amount can be obtained from the output of the magnetic sensor 10. That is, there is a predetermined relationship between the voltage of the resonant circuit, the oscillation frequency of the resonant circuit, and the distance between the coin and the resonant coil 11. There is also a predetermined relationship between the distance between the coin and the resonant coil 11 and the distance feature amount. Specifically, as shown in Figure 19, the greater the distance between the coin and the resonant coil 11, the smaller the distance feature amount becomes. This is because when the coin is farther away from the resonant coil 11, it is farther away from the magnetic field generated by the coil 11. Therefore, a table based on the three-way relationship between the voltage and oscillation frequency of the resonant circuit and the distance between the coin and the resonant coil 11, and a table showing the relationship between the distance between the coin and the resonant coil 11 and the distance feature are prepared in advance, and the distance between the coin and the resonant coil 11 is obtained by comparing the output of the magnetic sensor 10, i.e., the voltage and oscillation frequency of the resonant circuit, with the former table, and the distance feature of the coin is obtained by comparing the distance between the coin and the resonant coil 11 with the latter table. The second waveform data can be generated by using the voltage and oscillation frequency of the resonant circuit related to the output of the magnetic sensor 10 at successive different timings and these tables to obtain the distance feature at each timing. The relationship shown in FIG. 19 was obtained by taking data while changing the distance between the resonant coil 11 and the surface of the coin in advance, for example, to 0 mm, 0.2 mm, and 0.3 mm. In addition, a table showing such a relationship is prepared for each denomination, and the table for the corresponding denomination is used when obtaining the distance feature.

In this embodiment, the second material detection unit 23 detects a second material feature corresponding to the center of the coin, in this case the inner part of the bimetal coin, from the first waveform data generated by the first waveform generation unit 21 based on the second waveform data generated by the second waveform generation unit 25.

Figure 20 is a graph that shows a schematic diagram of another example of the first waveform data generated by the first waveform generating unit. Figure 20 is the same as Figure 10, and shows an example of the case where a bimetal coin is detected. For example, the second material detection unit 23 may detect a third point P3 and a fourth point P4 corresponding to both ends of the coin in the conveying direction in the second waveform data, as shown in Figure 18. More specifically, the second material detection unit 23 may search for points where the distance feature exceeds a predetermined third threshold Th2 in order from the start and end of the second waveform data, and may determine the points where the distance feature exceeds the third threshold Th2 as the third point P3 and the fourth point P4, respectively. Then, as shown in Figure 20, the second material detection unit 23 may detect the material feature of the center point Pc between the fifth point P5 and the sixth point P6 corresponding to the third point P3 and the fourth point P4, respectively, in the first waveform data as the second material feature. This makes it possible to detect the second material feature from the overall image of the first and second waveform data, and to detect the material feature of the inner part of the bimetal coin with high accuracy.

Next, the operation of the coin identification device according to this embodiment will be described with reference to FIG. 21. FIG. 21 is a flowchart illustrating an example of the operation of the coin identification device according to the second embodiment.

As shown in FIG. 21, first, steps S11 to S12 are executed as in the first embodiment.

Next, the second waveform generating unit 25 generates second waveform data representing the distance feature from the output of the magnetic sensor 10 (step S41).

Note that the timing of steps S12 and S41 may be reversed or may occur simultaneously.

Next, as in the first embodiment, the first material detection unit 22 executes the process of step S13. The first material detection unit 22 (step S13) may detect the first material feature amount according to the flow shown in FIG. 13.

Next, the second material detection unit 23 detects a second material feature corresponding to the center of the coin from the first waveform data generated in step S12 based on the second waveform data generated in step S41 (step S42).

Note that the timing of steps S13 and S42 may be reversed or may occur simultaneously.

Then, the recognition processing unit 24 performs a coin recognition process based on the first material characteristic detected in step S13 and the second material characteristic detected in step S42 (step S15), and the operation of the coin recognition device 2 ends.

FIG. 22 is a flowchart illustrating an example of the operation of the second material detection unit according to the second embodiment. The second material detection unit 23 (step S42) may detect the second material feature amount according to the flow shown in FIG. 22.

In this case, first, the second material detection unit 23 detects the third point P3 and the fourth point P4 corresponding to both ends of the coin in the conveying direction using the second waveform data (step S51).

Then, the second material detection unit 23 detects the fifth point P5 and the sixth point P6, which correspond to the third point P3 and the fourth point P4, respectively, in the first waveform data, and the center point Pc between the fifth point P5 and the sixth point P6, and detects the material characteristic of the center point Pc as the second material characteristic (step S52), and the process of detecting the second material characteristic is completed.

Next, we will explain a more specific embodiment of the coin identification device 2 according to this embodiment.

In this embodiment, as in the first embodiment, the magnetic sensor 10 may further include a resonant circuit 13 and a detection circuit 14 in addition to the resonant coil 11, as shown in FIG. 14.

Figure 23 is a block diagram illustrating another example of the configuration of the coin identification device according to the second embodiment. In addition to the above-mentioned magnetic sensor 10, first waveform generating unit 21, second waveform generating unit 25, first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), second material detection unit 23 and identification processing unit 24, the coin identification device 2 may further include an AD converter 30, a memory unit 40 and a control unit (arithmetic processing unit) 20, as in the first embodiment, as shown in Figure 23.

The control unit 20 can function as the above-mentioned first waveform generating unit 21, second waveform generating unit 25, first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), second material detection unit 23, and identification processing unit 24 by executing corresponding programs.

The second waveform generating unit 25 generates second waveform data based on the sampling data stored in the memory unit 40, i.e., a time series obtained by sampling the output of the magnetic sensor 10 at a predetermined time interval.

Next, the operation of the coin identification device according to this embodiment shown in FIG. 23 will be described with reference to FIG. 24. FIG. 24 is a flowchart illustrating another example of the operation of the coin identification device according to the second embodiment.

First, execute the processes of steps S11, S31, and S32 as in the first embodiment.

Next, the second waveform generating unit 25 generates second waveform data representing distance features from the sampling data (time series) sampled by the AD converter 30 (step S61).

Note that the timing of steps S32 and S61 may be reversed or may occur simultaneously.

Then, similar to the case shown in FIG. 21, the processing of steps S13, S42 and S15 is executed, and the operation of the coin identification device 2 is terminated.

The first material detection unit 22 (step S13) may detect the first material feature amount according to the flow shown in FIG. 13.

The second material detection unit 23 (step S42) may also detect the second material feature amount according to the flow shown in FIG. 22.

Furthermore, in step S15, as in the first embodiment, the identification processing unit 24 may compare the first material characteristic amount and the second material characteristic amount with reference data, and may determine that the coin is a genuine bimetal coin if the first material characteristic amount and the second material characteristic amount match the reference data. The identification processing unit 24 may also determine the denomination of the coin by comparing the first material characteristic amount and the second material characteristic amount with the reference data. Furthermore, the identification processing unit 24 may compare the first material characteristic amount and the second material characteristic amount with each other to determine whether the material of the outer edge of the coin is the same as the material of the center of the coin.

As described above, the above embodiment includes a magnetic sensor 10 including a resonant coil 11 that detects the magnetic properties of the coin being transported, a first waveform generating unit 21 that generates first waveform data representing a material characteristic from the output of the magnetic sensor 10, a first material detecting unit 22 that detects a first material characteristic corresponding to the outer edge of the coin from the first waveform data, a second material detecting unit 23 that detects a second material characteristic corresponding to the center of the coin from the first waveform data, and an identification processing unit 24 that performs an identification process of the coin based on the first material characteristic and the second material characteristic, making it possible to accurately identify bimetal coins with a simpler configuration.

Below, we will explain the variations of each embodiment.

In the above embodiment, the case of identifying bimetallic coins has been mainly described, but the device and method of the present disclosure can also identify monometallic coins. The device and method use a stable region with a small slope of the waveform of the first waveform data rather than differences in material, so it is possible to detect the position of the outer edge of the coin regardless of the coin's structure, and can detect with high accuracy the material characteristics of the outer edge of coins that have a different internal structure from bicolor coins such as monometallic coins and clad coins.

Figure 25 is a graph that shows a schematic example of the first waveform data, its first-order differential data, and its second-order differential data. Figure 25 shows an example of the case where a monometal coin is detected, and the top part shows a side cross section of the monometal coin M at the corresponding position on the graph. As shown in Figure 25, even in the case of a monometal coin M, the first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b) can detect the first point P1 and the second point P2 based on the first waveform data, the first-order differential data, and the second-order differential data, as in the case shown in Figure 11, and can detect the material characteristic amount SMx at the point Px where the differential value in the first-order differential data is the smallest, as the first material characteristic amount corresponding to the outer edge of the monometal coin M.

Furthermore, similar to the cases shown in Figures 18 and 20, the second material detection unit 23 can detect the third point P3 and the fourth point P4 based on the second waveform data, and detect the material characteristic at the center point Pc between the fifth point P5 and the sixth point P6 based on the first waveform data as the second material characteristic corresponding to the center of the monometal coin M.

In the above embodiment, the magnetic sensor 10 equipped with the resonant coil 11 is used alone as a sensor, but the magnetic sensor 10 may be used in combination with a material sensor (magnetic sensor) of another frequency. This makes it possible to detect monometal coins, plated clad coins, three-layer clad coins, bicolor clad coins, etc.

In the above embodiment, the first and second waveform data are generated based on a time series obtained by sampling the output of the magnetic sensor 10 at a predetermined time interval, i.e., based on data obtained by sampling the output of the magnetic sensor 10 according to time. However, the diameter of the coin may be detected using a diameter detection sensor (e.g., a magnetic sensor or an optical sensor) that detects the diameter of the coin, and the first and second waveform data may be generated based on data obtained by sampling the output of the magnetic sensor 10 according to changes in the diameter of the coin (output of the diameter detection sensor). This makes it possible to collect data similar to that collected when the coin is normally transported at a constant speed, even if the coin transport speed fluctuates or if the coin temporarily stops on the magnetic sensor 10 and then starts moving again. In this case, a time series obtained by sampling the output of the magnetic sensor 10 at a predetermined time interval may be collected, and only necessary elements may be selected and used from the time series based on the output of the diameter detection sensor.

In the above embodiment, the first waveform data, first-order differential data, and second-order differential data are processed in sequence from the beginning, but the first waveform data, first-order differential data, and second-order differential data may also be processed in sequence from the end, and even in this case, it is possible to detect the material features of the outer edge and center of the coin using a similar method.

In the above embodiment, a case has been described in which the coin is made of a non-magnetic material, but according to the device and method disclosed herein, even if the coin contains a magnetic material (e.g., a ferromagnetic material), it is possible to detect the material characteristics of the outer edge and center of the coin using a similar technique, and to identify the coin. For example, each part or at least a part of a bimetal coin may be made of a magnetic material (e.g., a ferromagnetic material). Even in this case, for example, it is possible to use the above-mentioned threshold value as is.

Although the embodiments have been described above with reference to the drawings, the present disclosure is not limited to the above-described embodiments. Furthermore, the configurations of the respective embodiments may be appropriately combined or modified without departing from the spirit and scope of the present disclosure.

As described above, the present disclosure provides a technology that is useful for accurately identifying bimetal coins with a simpler configuration.

1, 2: Coin identification device 10: Magnetic sensor 11: Resonant coil 12: Pot core 13: Resonant circuit 14: Detection circuit 20: Control unit (arithmetic processing unit)
21: First waveform generating unit 22: First material detection unit 22a: First order differential processing unit 22b: Second order differential processing unit 23: Second material detection unit 24: Identification processing unit 25: Second waveform generating unit 30: AD converter 40: Memory unit 51: Bicolor coin 51a, 53a: Core portion 51b, 53b: Ring portion 52: Clad coin 52a, 53a1: Core material 52b, 53a2: Surface layer 53: Bicolor clad coin 110: Conveying path 111: Conveying surface 112: Guide surface B: Bimetal coin Ba: Inner portion Bb: Outer portion C: Coin M: Monometal coin

Claims (12)

a magnetic sensor including a resonant coil for detecting magnetic characteristics of the coin being conveyed;
a first waveform generating unit that generates first waveform data representing a material feature, which is a feature that changes depending on the material of the coin, from an output of the magnetic sensor when a signal of a resonant frequency is applied to the single resonant coil;
a first material detection unit that detects a first material feature corresponding to an outer edge of the coin from the first waveform data;
a second material detection unit that detects a second material feature corresponding to a center portion of the coin from the first waveform data;
a coin recognition processing unit that recognizes the coin based on the first material characteristic amount and the second material characteristic amount;
A coin identification device comprising: The coin identification device according to claim 1, characterized in that the first material detection unit includes a first-order differential processing unit that performs differential processing on the first waveform data to generate first-order differential data, and a second-order differential processing unit that performs differential processing on the first-order differential data to generate second-order differential data, and detects the first material characteristic based on the first-order differential data and the second-order differential data. The coin identification device according to claim 2, characterized in that the first material detection unit detects a first point where a differential value in the first-order differential data is less than a predetermined first threshold value and where a differential value in the second-order differential data exceeds a predetermined second threshold value. The coin identification device of claim 3, characterized in that the first material detection unit detects the first material feature based on multiple material feature values from the first point to the second point in the first waveform data when a predetermined number of points in the first derivative data where the derivative value is less than the first threshold value are consecutive from the first point to the second point. The coin identification device according to claim 4, characterized in that the first material detection unit detects the material characteristic of the first waveform data at a point in the first derivative data where the differential value is the smallest among the predetermined number of points from the first point to the second point as the first material characteristic. The coin identification device according to any one of claims 1 to 5, further comprising a second waveform generating unit that generates second waveform data representing a distance feature, which is a feature that changes according to the unevenness of the coin, from the output of the magnetic sensor. The coin identification device of claim 6, characterized in that the second material detection unit detects a third point and a fourth point in the second waveform data corresponding to both ends of the coin in the transport direction, and detects the material characteristic of a center point between a fifth point and a sixth point corresponding to the third point and the fourth point, respectively, in the first waveform data as the second material characteristic. The coin identification device according to any one of claims 1 to 7, characterized in that the identification processing unit determines whether the coin is a bimetal coin based on the first material characteristic and the second material characteristic. A storage unit is further provided for storing reference data relating to material characteristics of the outer edge and center of a genuine bimetal coin,
9. The coin identification device according to claim 1, wherein the identification processing unit compares the first material characteristic amount and the second material characteristic amount with the reference data. The coin identification device according to claim 9, characterized in that the identification processing unit determines that the coin is a genuine bimetal coin if the first material characteristic and the second material characteristic match the reference data. The coin identification device according to claim 9 or 10, characterized in that the identification processing unit determines the denomination of the coin by comparing the first material characteristic and the second material characteristic with the reference data. A coin identification method using a magnetic sensor, comprising:
The magnetic sensor includes a resonant coil.
detecting a magnetic characteristic of a coin being conveyed by the magnetic sensor;
generating first waveform data representing a material feature, which is a feature that changes depending on the material of the coin, from an output of the magnetic sensor when a signal of a resonant frequency is applied to the single resonant coil;
detecting a first material feature corresponding to an outer edge of the coin from the first waveform data;
detecting a second material feature corresponding to a central portion of the coin from the first waveform data;
performing a coin identification process based on the first material characteristic amount and the second material characteristic amount;
A coin identification method comprising:

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